DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals
arXiv:2606.11125v1 Announce Type: cross Abstract: Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cost, wearable-friendly cuffless BP estimation. However, even with recent progress, many PPG-based models are trained with BP regression alone and may rely on amplitude-dominated shortcuts. In addition, demographic covariates that systematically modulate vascular compliance are often incorporated only via late fusion, limiting subject-specific representation learning. We propose a Transformer-based n
The proliferation of wearable technology and advancements in AI/Transformer models are converging to enable more sophisticated health monitoring solutions.
Cuffless and continuous blood pressure monitoring offers significant potential for personalized healthcare, early detection of cardiovascular issues, and improved long-term health management.
This research outlines a more robust methodology for blood pressure estimation from PPG signals, moving beyond amplitude-dominated shortcuts and integrating demographic data for better accuracy.
- · Wearable tech companies
- · Healthcare providers
- · Cardiovascular patients
- · AI/ML researchers in health
- · Traditional medical device manufacturers (long term)
- · Companies relying on less accurate PPG models
Improved accuracy and reliability of cuffless blood pressure monitors.
Increased adoption of continuous, passive health monitoring in everyday life.
Transformation of preventative medicine with AI-driven, real-time physiological insights leading to predictive health interventions.
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Read at arXiv cs.LG